saved_results <- readRDS("../output/AZ_06-03-2020_ens.rds")
res <- saved_results$res |>
filter(!is.na(pred))
baseline <- saved_results$baseline
mean_pred <- res |> group_by(date) |>
summarize(pred = mean(pred))
plt <- ggplot(res) +
geom_line(mapping = aes(x = date, y = pred, group = iter, color = "Ensemble Member"), linewidth = 1) +
geom_line(data = mean_pred, mapping = aes(x = date, y = pred, color = "Mean Prediction"), linewidth = 1.5) +
geom_line(baseline$dat |>
filter(date <= baseline$forecast_date + 30),
mapping = aes(x = date, y = smoothedIncidence, color = "Time-Averaged Data"), linewidth = 1)+
geom_line(baseline$dat |>
filter(date <= baseline$forecast_date + 30),
mapping = aes(x = date, y = positiveIncrease, color = "Raw Data")) +
geom_point(baseline$dat |>
filter(status %in% c("available")),
mapping = aes(x = date, y = positiveIncrease, color = "Used for Fitting")) +
scale_color_manual(values = c("Ensemble Member" = "pink",
"Mean Prediction" = "deeppink2",
"Time-Averaged Data" = "black",
"Raw Data" = "black",
"Used for Fitting" = "deepskyblue3"))+
theme_bw() +
theme(legend.position = "top") +
labs(x = "Date", y = "Incident Cases", color = element_blank()) +
lims(y = c(0,10000))
plt